library(timetk)
library(inspectdf)
library(janitor)
library(readr)
library(dplyr)
library(readr)
library(ggplot2)
library(naniar)
library(packcircles)
library(ggridges)
library(ggbeeswarm)
library(patchwork)
library(dplyr)
library(tidyr)
library(readr)
library(skimr)
library(purrr)
library(stringr)
library(urltools)
library(magrittr)
library(lubridate)
library(janitor)
Az adatsor Tetouan City 2017-es áramfogyasztásának alakulását írja le 10 percenkénti bontásban 3 zónára.
Salam, A., & El Hibaoui, A. (2018, December). Comparison of Machine Learning Algorithms for the Power Consumption Prediction:-Case Study of Tetouan city–. In 2018 6th International Renewable and Sustainable Energy Conference (IRSEC) (pp. 1-5). IEEE
az áramfogyasztás előrejelzése idősor elemzéssel
url <-
"https://raw.githubusercontent.com/fzksmrk/adatbanyaszati-beadando-2/main/data.csv"
data <- readr::read_csv(
url,
col_types = cols(
DateTime = col_datetime(format = "%m/%d/%Y %H:%M"),
Temperature = col_double(),
Humidity = col_double(),
`Wind Speed` = col_double(),
`general diffuse flows` = col_double(),
`diffuse flows` = col_double(),
`Zone 1 Power Consumption` = col_double(),
`Zone 2 Power Consumption` = col_double(),
`Zone 3 Power Consumption` = col_double()
)
)
data
data <- janitor::clean_names(data)
data <- dplyr::rename(data,"zone_1" = "zone_1_power_consumption","zone_2" = "zone_2_power_consumption","zone_3" = "zone_3_power_consumption")
data
data <- data %>%
timetk::tk_augment_timeseries_signature(date_time) %>%
select(
-matches(
"(half)|(wday)|(mday)|(qday)|(mday)|(yday)|(mweek)|(xts)|(second)|(minute)|(iso)|(num)|(hour12)|(am.pm)|(week\\d)|(mday7)"
)
) %>%
select(-diff)
inspectdf::inspect_num(data)
data %>%
ggplot(aes(date_time, temperature)) +
geom_line() +
labs(
x = NULL,
y = "Temperature"
) -> p1
data %>%
ggplot(aes(date_time, humidity)) +
geom_line() +
labs(
x = NULL,
y = "Humidity"
) -> p2
data %>%
ggplot(aes(date_time, wind_speed)) +
geom_line() +
labs(
x = NULL,
y = "Wind Speed"
) -> p3
data %>%
select(date_time, contains("flows")) %>%
pivot_longer(-date_time) %>%
ggplot(aes(date_time, value)) +
geom_line(aes(color = name)) +
labs(
x = NULL,
y = "Flows",
color = "Flows"
) -> p4
p1 / p2 / p3 / p4
data %>%
select(date_time, contains("zone")) %>%
pivot_longer(-date_time) %>%
ggplot(aes(date_time, value)) +
geom_line(aes(color = name)) +
scale_y_continuous(labels = scales::label_number_si()) +
labs(
x = NULL,
y = "Power",
color = "Zone"
)
decompose_ts <- function(series, freq = 144) {
ts_obj <- ts(series, frequency = freq)
decompose(ts_obj)
}
plot(decompose_ts(data$zone_1))
plot(decompose_ts(data$zone_2))
plot(decompose_ts(data$zone_3))
plot(decompose_ts(data$temperature))
plot(decompose_ts(data$wind_speed))
plot(decompose_ts(data$humidity))
plot(decompose_ts(data$general_diffuse_flows))
plot(decompose_ts(data$diffuse_flows))